The new tool supplies a shared description that standardizes how Kubernetes structures like pod templates, statuses, and replica counts are parsed. Distributed via a custom resource definition named Karta and an accompanying Go library, it eliminates the need to build individual adapters for frameworks such as JobSet or LeaderWorkerSet. Platform tools like the KAI Scheduler and NVIDIA Run:ai are already using these definitions to enforce gang scheduling and streamline cluster management. This unified model ensures integrated systems can seamlessly read complex multi-node resource requests like aggregate GPU allocations.
Key points
Centralizing workload models removes the need for continuous, release-dependent adapter d…: Centralizing workload models removes the need for continuous, release-dependent adapter development across Kubernetes tooling.
Developers can write a single structural description that works across inspection, gang-s…: Developers can write a single structural description that works across inspection, gang-scheduling, and platform visibility platforms.
NVIDIA Run:ai utilizes these unified definitions to enhance workload management and visib…: NVIDIA Run:ai utilizes these unified definitions to enhance workload management and visibility across multi-node GPU clusters.
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NVIDIA DSX AI Factory Platform brings together technologies to help the ecosystem design, build, and operate more efficient AI factories, including open source, modular software components built to support multi-tenant operations and scaling.
Operating an AI factory requires a shared understanding of distributed workloads: their components, pod templates, replica counts, and status. On Kubernetes, each framework encodes that information differently. JobSet, LeaderWorkerSet, and other custom resources often require separate adapters before a tool can interpret and manage them.
NVIDIA Workload Map provides a common description of Kubernetes workload structure that users can reuse across frameworks. As part of the NVIDIA DSX AI Factory Platform, Workload Map enables integrated schedulers, controllers, and platforms to interpret supported workload types without maintaining separate framework-specific parsing code.
This post shows how to describe a distributed JobSet workload once, then reuse that definition for workload inspection, gang scheduling, and platform visibility.
Why every workload type needs its own adapter
On Kubernetes, a real workload is rarely a single pod. A distributed training job or a multi-node inference server is a small tree of resources: groups…
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